ArticleMethods in molecular biology (Clifton, N.J.)2025
Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
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Abstract
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantitative Structure-Activity Relationship (QSAR) models was presented and implemented in two freely available and easy-to-use workflows. The first workflow helps the user retrieving chemical data (SMILES) from the web, checking their correctness and curating them to produce consistent and ready-to-use datasets for cheminformatic. The second workflow implements six machine learning methods to develop classification QSAR models. Models can be additionally used to predict external chemicals. Calculation and selection of chemical descriptors, tuning of models' hyperparameters, and methods to handle data unbalancing are also incorporated in the workflow. Both the workflows are implemented in KNIME and represent a useful tool for computational scientists, as well as an intuitive and straightforward introduction to QSAR.
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